Face recognition is one of the most difficult and challenging tasks in computer vision, partly because of large variations in human faces; this is particularly true when only a portion of a face is available for analysis. Researchers have been developing technologies for face recognition based on partial-face images due to the demands of many real-world operating scenarios that require accurate, efficient, uncooperative, and cost-effective solutions. Some of these technologies identify local binary patterns (LBP) in raw pixel intensities but nonetheless fail to achieve accurate, efficient, uncooperative, and cost-effective solutions, largely due to the minimal amount of useful data that can be provided by a partial-face image.
To achieve an accurate, efficient, uncooperative, and cost-effective solution to the problem of face recognition in situations where only a portion of a face is available for analysis, it becomes necessary to extract as much unique information as possible from each image in question and to use such information in an exhaustive comparison. However, these methods are known to be computationally expensive and may require special tweaking in order to generate meaningful results. More accurate and efficient face recognition methods are desired in numerous applications, which demand near real-time computation and do not require user cooperation. Applications include automated face recognition in surveillance images and access control, among others.